Analysis of the efficiency of the linearization techniques for solving multi-objective linear fractional programming problems by goal programming

This paper presents and analyzes the applicability of three linearization techniques used for solving multi-objective linear fractional programming problems using the goal programming method. The three linearization techniques are: (1) Taylor’s polynomial linearization approximation, (2) the method of variable change, and (3) a modification of the method of variable change proposed in [20]. All three linearization techniques are presented and analyzed in two variants: (a) using the optimal value of the objective functions as the decision makers’ aspirations, and (b) the decision makers’ aspirations are given by the decision makers. As the criteria for the analysis we use the efficiency of the obtained solutions and the difficulties the analyst comes upon in preparing the linearization models. To analyze the applicability of the linearization techniques incorporated in the linear goal programming method we use an example of a financial structure optimization problem

Observation of temporary accommodation for construction workers according to the code of practice for temporary construction site workers amenities and accommodation (ms2593:2015) in Johor, Malaysia

The Malaysian government is currently improving the quality of workers temporary accommodation by introducing MS2593:2015 (Code of Practice for Temporary Site Workers Amenities and Accommodation) in 2015. It is in line with the initiative in the Construction Industry Transformation Programme (2016-2020) to increase the quality and well-being of construction workers in Malaysia. Thus, to gauge the current practice of temporary accommodation on complying with the particular guideline, this paper has put forth the observation of such accommodation towards elements in Section 3 within MS2593:2015. A total of seventeen (17) temporary accommodation provided by Grade 6 and Grade 7 contractors in Johor were selected and assessed. The results disclosed that most of the temporary accommodation was not complying with the guideline, where only thirteen (13) out of fifty-eight (58) elements have recorded full compliance (100%), and the lowest compliance percentage (5.9%) are discovered in the Section 3.12 (Signage). In a nutshell, given the significant gap of compliance between current practices of temporary accommodation and MS2593:2015, a holistic initiative need to be in place for the guideline to be worthwhile

Experts’ consensus to identify elements of career management competencies in Work-Based Learning (WBL) program using Fuzzy Delphi Analysis

This study aimed to obtain experts‘ opinion and consensus on the elements of career management competencies that can be developed through the Work-Based Learning (WBL) program in polytechnic

A compromise-based particle swarm optimization algorithm for solving Bi-level programming problems with fuzzy parameters

© 2015 IEEE. Bi-level programming has arisen to handle decentralized decision-making problems that feature interactive decision entities distributed throughout a bi-level hierarchy. Fuzzy parameters often appear in such a problem in applications and this is called a fuzzy bi-level programming problem. Since the existing approaches lack universality in solving such problems, this study aims to develop a particle swarm optimization (PSO) algorithm to solve fuzzy bi-level programming problems in the linear and nonlinear versions. In this paper, we first present a general fuzzy bi-level programming problem and discuss related theoretical properties based on a fuzzy number ranking method commonly used. A PSO algorithm is then developed to solve the fuzzy bi-level programming problem based on different compromised selections by decision entities on the feasible degree for constraint conditions under fuzziness. Lastly, an illustrative numerical example and two benchmark examples are adopted to state the effectiveness of the compromise-based PSO algorithm

Hyperspectral Unmixing Overview: Geometrical, Statistical, and Sparse Regression-Based Approaches

Imaging spectrometers measure electromagnetic energy scattered in their instantaneous field view in hundreds or thousands of spectral channels with higher spectral resolution than multispectral cameras. Imaging spectrometers are therefore often referred to as hyperspectral cameras (HSCs). Higher spectral resolution enables material identification via spectroscopic analysis, which facilitates countless applications that require identifying materials in scenarios unsuitable for classical spectroscopic analysis. Due to low spatial resolution of HSCs, microscopic material mixing, and multiple scattering, spectra measured by HSCs are mixtures of spectra of materials in a scene. Thus, accurate estimation requires unmixing. Pixels are assumed to be mixtures of a few materials, called endmembers. Unmixing involves estimating all or some of: the number of endmembers, their spectral signatures, and their abundances at each pixel. Unmixing is a challenging, ill-posed inverse problem because of model inaccuracies, observation noise, environmental conditions, endmember variability, and data set size. Researchers have devised and investigated many models searching for robust, stable, tractable, and accurate unmixing algorithms. This paper presents an overview of unmixing methods from the time of Keshava and Mustard's unmixing tutorial [1] to the present. Mixing models are first discussed. Signal-subspace, geometrical, statistical, sparsity-based, and spatial-contextual unmixing algorithms are described. Mathematical problems and potential solutions are described. Algorithm characteristics are illustrated experimentally.Comment: This work has been accepted for publication in IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensin

FORMULATION OF CONCAVE-CONVEX FRACTIONAL PROGRAMMING MODEL FOR BANK PORTFOLIO SELECTIONS

In this paper, a concave-convex fractional programming model for bank portfolio selections is formulated. We have transformed the model into a concave quadratic programming problem and developed a technique for its solution. A real life application of the model is performed with twelve banks in Nigeria. The optimal solution determined by the proportion of investment to be made by an investor in each bank in order to maximize the expected returns at minimum risk is highlighted. However, the computational results show that the proposed model can generate a favourable portfolio strategy according to the investor’s satisfactory degree.  The trade-off curve also indicates the amount of risk that is commensurate with a particular expected return. Key words: concave-convex, fractional programming problem, optimization, transformatio

Design of an Analysis Model for Strategic Behavior in the Digital Economy

Nowadays, multi-criteria decision-making techniques are highly developed, and are widely applied in multiple fields. They model and solve decisional problems by optimising multiple conflicting objectives. These techniques are very useful because they simultaneously analyse all the different criteria, and select the best alternatives according to the decision-maker’s objectives and preferences. An important issue in this context is the adequacy of the structure of corporate long-term financing and its potential impact on the sustainable development of the long-term business plan. The purpose of this study is to advance the analysis of these strategic decisions, measuring the a posteriori results and analysing their coherence with the strategies followed a priori. To do this, sustainable strategic decisions will be mathematically modelled and parametrised, creating a system to study the preferences followed and to describe the corporate behaviour. This system is applied as a case example for two leading companies in the digital sector, and the corresponding results over the last few years are evaluated